code: def nanmin(
      a: paddle.Tensor,
      /,
      *,
      axis: Optional[Union[int, Tuple[int]]] = None,
      keepdims: Optional[bool] = False,
      initial: Optional[Union[int, float, complex]] = None,
      where: Optional[paddle.Tensor] = None,
      out: Optional[paddle.Tensor] = None,
  ) -> paddle.Tensor:
      nan_mask = paddle.isnan(a)
      if where is not None:
          nan_mask = paddle.logical_or(
              nan_mask, paddle.logical_not(where)
          )
      a_copy = a.clone()
      a_copy = paddle.where(
          nan_mask, paddle.full_like(a_copy, float("inf")), a_copy
      )
      if axis is None:
          result = paddle.min(a_copy, keepdim=keepdims)
      else:
          result = paddle.min(a_copy, axis=axis, keepdim=keepdims)
      if initial is not None:
          initial = paddle.to_tensor(initial, dtype=a.dtype)
          result = paddle.minimum(result, initial)
      return result
  
imports: from typing import Optional, Union, Tuple, Sequence, Any
  import paddle
  import ivy.functional.backends.paddle
  import ivy
  from copy import deepcopy
  from ivy.func_wrapper import with_unsupported_device_and_dtypes, with_supported_dtypes
  from .None import backend_version
label_desc: Use image node_id 5 for calling a global function with example usage: nanmin() and returns: result